NutriPlot

Overview

How to read this scatter plot, Figure 1:

On the axis, as you move towards the right, the food becomes more calorie-dense. Similarly, moving upwards indicates increasing protein density.

Consequently:

• Top left indicates foods with high protein content per calorie and low calorie count per 100g.
  
• Bottom right represents foods with low protein content per calorie but high overall calorie count per 100g.
  
• Top right denotes foods with high protein content per calorie and high calorie count per 100g.
  
• Bottom left signifies foods with low protein content per calorie and low calorie count per 100g.

source("libraries.R")

df <- read_csv2("prot.csv", show_col_types = FALSE)

scatter_plot1 <- df %>% 
  ggplot() +
  # Add points to the plot with kcal on x-axis and perc_prot on y-axis
  geom_point(aes(x = kcal, y = perc_prot, color = perc_prot, text = name)) +    
  # Add text labels using geom_text_repel
  geom_text_repel(aes(x = kcal, y = perc_prot, label = name), force = 6) +  
  scale_x_continuous(n.breaks = 10) +
  scale_y_continuous(n.breaks = 10) +
  scale_color_gradient(low = 'red',high = "green") +
  theme(legend.position = "none") +    
  labs(y = "protein[%] for 100 kcal", x = "kcal", title = "For 100g")    

scatter_plot1

Figure 1: for 100g of content

scatter_plot2 <- df %>%
  ggplot() +
  geom_point(aes(y = perc_prot, 
                 x = perc_prot,
                 color = perc_prot,
                 text= paste("Name: ", name, "\n",
                             "protein[%]:", perc_prot))) +
  scale_color_gradient(low = 'red',high = "green") +
  scale_y_continuous(n.breaks = 10) +
  scale_x_continuous(n.breaks = 10) +
  theme(legend.position = "none") +
  labs(y = "protein[%] for 100 kcal", x = "name", title = "For 100g")       

# scatter_plot2

ggplotly(scatter_plot2,tooltip = "text")

Figure 2: Caption